Decoding Silent Speech Based on High-Density Surface Electromyogram Using Spatiotemporal Neural Network

Decoding Silent Speech Based on High-Density Surface Electromyogram Using Spatiotemporal Neural Network

Finer-grained decoding at a phoneme or syllable level is a key technology for continuous recognition of silent speech based on surface electromyogram (sEMG). This paper aims at developing a novel syllable-level decoding method for continuous silent speech recognition (SSR) using spatio-temporal end-...

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Bibliographic Details
Main Authors: Xi Chen, Xu Zhang, Xiang Chen, Xun Chen
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
Silent speech recognition
high-density surface electromyography
spatiotemporal feature
language model
time sequence decoding
Online Access:https://ieeexplore.ieee.org/document/10098814/

Internet

https://ieeexplore.ieee.org/document/10098814/

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